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Record W4411490540 · doi:10.1148/ryct.240200

Pericoronary Adipose Tissue Attenuation in Patients with Future Acute Coronary Syndromes: The ICONIC Study

2025· article· en· W4411490540 on OpenAlexaff
Alan C. Kwan, Evangelos Tzolos, Eyal Klein, Donghee Han, Andrew Lin, Keiichiro Kuronuma, Billy Chen, Guadalupe Flores Tomasino, Heidi Gransar, Piotr J. Slomka, Cathérine Gebhard, Philipp A. Kaufmann, Jeroen J. Bax, Filippo Cademartiri, Kavitha M. Chinnaiyan, Benjamin J.W. Chow, Edoardo Conte, Ricardo C. Cury, Gudrun Feuchtner, Martin Hadamitzky, Yong‐Jin Kim, Jonathon Leipsic, Erica Maffei, Hugo Marques, Fabian Plank, Gianluca Pontone, Todd C. Villines, Mouaz H. Al‐Mallah, Pedro de Araújo Gonçalves, Ibrahim Danad, Yao Lu, Ji Hyun Lee, Sang‐Eun Lee, Lohendran Baskaran, Subhi J. Al’Aref, Matthew J. Budoff, Habib Samady, Peter H. Stone, Renu Virmani, Stephan Achenbach, Jagat Narula, Hyuk‐Jae Chang, Leslee J. Shaw, Daniel S. Berman, Fay Y. Lin, Damini Dey

Bibliographic record

VenueRadiology Cardiothoracic Imaging · 2025
Typearticle
Languageen
FieldMedicine
TopicCardiovascular Disease and Adiposity
Canadian institutionsUniversity of British ColumbiaUniversity of Ottawa
FundersNational Heart, Lung, and Blood InstituteNational Institutes of HealthDoris Duke Charitable FoundationBritish Heart FoundationDr. Miriam and Sheldon G. Adelson Medical Research FoundationNational Medical Research Council
KeywordsMedicineAdipose tissueCoronary angiographyInternal medicineCohortCardiologyAttenuationAcute coronary syndromeMyocardial infarction

Abstract

fetched live from OpenAlex

Pericoronary adipose tissue attenuation, a marker of coronary inflammation, shows minimal quantitative differences between patients with and without future incident acute coronary syndromes in a matched case-control multicenter cohort of patients who underwent coronary CT angiography but is independently associated with future incident acute coronary syndromes in adjusted survival analyses.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.051
Threshold uncertainty score0.771

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.004
GPT teacher head0.265
Teacher spread0.260 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations3
Published2025
Admission routes1
Has abstractyes

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